We propose a fast motion search method for H.264/AVC with dual-path block size decision. H.264/AVC employs variable block sizes for motion compensation to reduce coding bits of inter-frame prediction error, which requires considerable amount of computation time when motion estimation is performed for every block size. Our algorithm
contains two strategies to reduce computation time for motion search; block size mode reduction and search range reduction. According to these strategies, our algorithm consists of two stages. At the first stage, RDCost-based block size mode reduction is conducted. Rate-distortion function (RDCost) for skip mode is calculated at first, which determines the smallest block size for motion search. The second stage is a fast variable block size motion estimation which contains two paths, 16x16-first and 8x8-first. The 16x16-first path is invoked when the minimum block size determined at the first stage is larger than 8x8. In the 8x8-first path, search range for blocks larger than 8x8 is reduced according to distance between motion vectors for 8x8 blocks. From our experiment using JM 8.5, it is confirmed that our algorithm can reduce about 89.3% of computation time as compared to JM, with only negligible PSNR degradation.
New synchrotron x-ray CT system with phase-contrast and fluorescent techniques are being developed for biomedical researches with the high-contrast and high-spatial resolution. We have applied these techniques for in-vivo and ex-vivo imaging. The phase-contrast x-ray CT (PCCT) was a highly sensitive imaging technique to depict the morphological information of the soft tissue in biological object, whereas fluorescent x-ray CT (FXCT) could depict the functional information concerning to specific heavy atomic number elements at very low content. Thus, the success of in-vivo imaging by PCCT and FXCT allows starting new approach to bio-imaging researches.
An approach to extract traffic events by integrating the low-level, middle-level, and high-level feature extraction modules is developed in this research. To be more specific, the low-level module extracts features such as motion, size, and location. The middle-level module builds a bridge between the road surface plane in the real world and the captured image plane by geometric analysis. Finally, the high-level module looks for traffic events such as "traffic jam", "lane
change", and "traffic rule violation", which require the understanding of the video contents in a specific knowledge
domain. In the high-level module, various traffic events are related to motion characteristics obtained from the middle-level module. It is demonstrated by experimental results that the proposed system can achieve robust traffic event extraction. The effectiveness of the proposed technique is analyzed. Conventional traffic event extraction methods demand the knowledge of capturing conditions for camera calibration. This requirement can be greatly relaxed in our proposed scheme.
Vision-based highway monitoring systems play an important role in transportation management and services owing to their powerful ability to extract a variety of information. Detection accuracy of vision-based systems is however sensitive to environmental factors such as lighting, shadow and weather conditions, and it is still a challenging problem to maintain detection robustness at all time. In this research, we present a novel method to enhance detection and tracking accuracy at the nighttime based on rear-view monitoring. In the meanwhile, a method is proposed to improve the background detection and extraction, which usually serves as the first step to moving object region detection. Finally, the effectiveness of the rear-view technique will be analyzed. We compare the tracking accuracy between the front-view and the rear-view techniques, and show that the proposed system can achieve higher detection accuracy at nighttime.
The vision-based traffic monitoring system provides an attractive solution in extracting various traffic parameters such as the count, speed, flow and concentration from the processing of video data captured by a camera system. The detection accuracy is however affected by various environment factors such as shadow, occlusion, and lighting. Among these, the occurrence of occlusion is one of the major problems. In this work, a new scheme is proposed to detect the occlusion and determine the exact location of each vehicle. The proposed algorithm is based on the matching of images from multiple cameras. In the proposed scheme, we do not need edge detection, region segmentation, and camera calibration operations, which often suffer from the variation of environmental conditions. Experimental results are given to verify that the proposed technique is effective for vision-based highway surveillance systems.
This paper proposes a dynamic bit rate control method for real-time video streaming over the Internet. It is based on the feedback mechanism using RTCP(RTP control protocol) which provides network congestion parameters such as inter-arrival jitter, fraction lost, and round trip time by SR (Sender Report)/RR( Receiver Report). The proposed method firstly detects network congestion, then the network state is categorized into four congestion levels by analyzing the network congestion parameters such as jitter and packet loss derived from RR packets arriving periodically, then the coding bit rate is determined according to the current congestion level. The proposed dynamic bit rate control mechanism has also been implemented in the MPEG-4 video transmission system. The experimental results show that the proposed method can successfully suppress the packet loss and control the coding bit rate appropriately even for a congested network which does not guarantee QoS such as bandwidth resources and/or maximum delay.
We are investigating possible medical applications of phase- contrast X-ray imaging using an X-ray interferometer. This paper introduces the strategy of the research project and the present status. The main subject is to broaden the observation area to enable in vivo observation. For this purpose, large X-ray interferometers were developed, and 2.5 cm X 1.5 cm interference patterns were generated using synchrotron X-rays. An improvement of the spatial resolution is also included in the project, and an X-ray interferometer designed for high-resolution phase-contrast X-ray imaging was fabricated and tested. In parallel with the instrumental developments, various soft tissues are observed by phase- contrast X-ray CT to find correspondence between the generated contrast and our histological knowledge. The observation done so far suggests that cancerous tissues are differentiated from normal tissues and that blood can produce phase contrast. Furthermore, this project includes exploring materials that modulate phase contrast for selective imaging.
We describe a method of moving object detection directly from MPEG coded data. Since motion information in MPEG coded data is determined in terms of coding efficiency point of view, it does not always provide real motion information of objects. We use a wide variety of coding information including motion vectors and DCT coefficients to estimate real object motion. Since such information can be directly obtained from coded bitstream, very fast operation can be expected. Moving objects are detected basically analyzing motion vectors and spatio-temporal correlation of motion in P-, and B-pictures. Moving objects are also detected in intra macroblocks by analyzing coding characteristics of intra macroblocks in P- and B-pictures and by investigating temporal motion continuity in I-pictures. The simulation results show that successful moving object detection has been performed on macroblock level using several test sequences. Since proposed method is very simple and requires much less computational power than the conventional object detection methods, it has a significant advantage as motion analysis tool.
In this paper, we propose scene decomposition algorithm from MPEG compressed video data. As a preprocessing for scene decomposition, partial reconstruction methods of DC image for P- and B-pictures as well as I-pictures directly from MPEG bitstream are used. As for detection algorithms, we have exploited several methods for detection of abrupt scene change, dissolve and wipe transitions using comparison of DC images between frames and coding information such as motion vectors. It is also proposed the method for exclusion of undesired detection such as flashlight in order to enhance scene change detection accuracy. It is shown that more than 95 percent of decomposition accuracy has been obtained in the experiment using more than one hour TV program. It is also found that in the proposed algorithm scene change detection can be performed more than 5 times faster than normal playback speed using 130MIPS workstation.